October 29-November 2, 2017
New York
Delivering on the promise of data science

TRACK TOPICS:

Track 1: BUSINESS
Analytics strategy & operationalization
Track 2: TECH
Predictive modeling methods
Track 3 (Day 1): MARKETING
Marketing & market research analytics
Track 3 (Day 2): CASE STUDIES
Varied business applications

SESSION LEVELS:

All level tracks Blue circle sessions are for All Levels   Red triangle sessions are Expert/Practitioner Level

Agenda Overview – New York 2017
Pre-Conference Workshops: Sunday, October 29, 2017
Full-day Workshop
Big Data: Proven Methods You Need
to Extract Big Value

Vladimir Barash, Graphika
Morning Session Workshop
R Bootcamp: For Newcomers to R
Max Kuhn, RStudio
Full-day Workshop
R for Predictive Modeling: A Hands-On Introduction
Max Kuhn, RStudio

DAY 1, Monday, October 30, 2017
(PAW Healthcare & PAW Financial run in parallel on this day - dual registration required)
8:00-8:45am Registration & Networking Breakfast
8:45-8:50am Conference Chair Welcome
Eric Siegel, Predictive Analytics World
8:50-9:40am
KEYNOTE
Analytics for the Job: Tips and Tricks for Success
Anne Robinson, Verizon Wireless
9:40-10:00am Diamond Sponsor Presentation
10:00-10:30am Exhibits & Morning Coffee Break
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling methods Track 3—MARKETING: Marketing & market research analytics
10:30-11:15am Crisis response; analytics management Hand-labeled training data Churn modeling
Lessons from:
NYC Mayor's Office
Quickly Building an Analytics Environment to Address a Public Health Crisis in NYC
All level tracks
Simon Rimmele, NYC Mayor's Office of Data Analytics
Case Study: Bloomberg L.P.
Crowd-Sourcing and Quality:
How To Get The Best Out of Hand-Tagged Training Data for Machine Learning Models
All level tracks
Leslie Barrett, Bloomberg L.P.
Case Study: Paychex
Retention Modeling in Uncertain Economic Times
All level tracks
Rob Rolleston, Paychex
11:20-11:40am Education and team building Time series modeling Market research and analytics
Lessons from:
LinkedIn
The Sprint for Teaching Data Science: LinkedIn Learning, Analytics, and the New Era of Just-In-Time Skills Training All level tracks

Steve Weiss, LinkedIn
Time Series Prediction with Twitter: A Case Study of Crime in New York City
Anasse Bari, George Washington University
Aaron McKinstry, Courant Institute of Mathematical Sciences of New York University
Chuan-Heng Lin, Enrolled at New York University
Gen Xiang, Trinnacle Capital Management
Case Study: Walmart
Relative Value of Implicit and Explicit Feedback in Predicting Customer Preferences

Jennifer Prendki, Walmart
11:40am-12:00pm Market research and analytics
Case Study: Verizon Wireless
Predicting Brand Love With Wireless Behaviors

Michael Gooch-Breault, Verizon Wireless
12:05-1:30pm Lunch in Exhibit Hall
1:30-2:15pm
KEYNOTE
The Predictability Predicament: Your Model Overlooks the Real Target

Claudia Perlich, Dstillery
2:15-2:35pm Gold Sponsor Presentations
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling methods Track 3—MARKETING: Marketing & market research analytics
2:40-3:00pm Analytics strategy Analytical methods Marketing applications
Lessons from:
The Clorox Company
Getting Started with Data Science Driven Insights, Execution and Innovation in the CPG Industry All level tracks

Payel Chowdhury, The Clorox Company
Machine Learning vs. Feature Engineering: What should the Focus be in Attempting to Predict Customer Behaviour
Richard Boire, Environics Analytics
Real-Time Automation to Build Relationships & Retain Customers
Kristina Pototska, TriggMine
3:05pm-3:25pm Acquisition for academic enrollment
Case Study: Becker College
Acquisition Funnel for Higher Education

Feyzi Bagirov, Becker College
3:25-3:55pm Exhibits & Afternoon Break
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling methods Track 3—MARKETING: Marketing & market research analytics
3:55-4:40pm Analytics strategy Analytical methods Churn modeling; uplift modeling
Lessons from:
Prudential Financial
Value Creation Through Analytics Innovation
All level tracks
Wayne Huang, Prudential Financial
Case Study: Citigroup
A Modified Logistic Regression Approach Enhanced by New Interactions and Scaling Detections through Random Forests and GBM

Yulin Ning, Citigroup
Case Study: The Co-operators
Which Predictive Model Will Best Help Increase Retention?

Emilie Lavoie-Charland, The Co-operators
4:45-5:30pm Analytics management Forecasting; analytical methods Optimizing outreach; uplift modeling
Lessons from:
Citigroup
Project Management for Data Scientists
All level tracks
Wanda Wang, Citigroup
Case Study: Micron Technology
Demand Forecasting with Machine Learning

Colin Ard, Micron Technology
Using Rapid Experiments and Uplift Modeling to Optimize Outreach at Scale
Daniel Porter, BlueLabs
5:30-7:00pm Networking Reception

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DAY 2, Tuesday, October 31, 2017
(PAW Healthcare & PAW Financial run in parallel on this day - dual registration required)
8:00-8:40am Registration & Networking Breakfast
8:40-8:45am Conference Chair Welcome
Eric Siegel, Predictive Analytics World
8:45-9:05am Special Plenary Session
What to Optimize? The Heart of Every Analytics Problem
Dr. John Elder, Elder Research, Inc.
9:05-9:15am Plenary Session
Industry Trends: Highlights from the 2017 Data Miner Survey
Karl Rexer, Rexer Analytics
9:15-10:00am Diamond Sponsor Presentation
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling methods Track 3—MORE CASE STUDIES: Varied business applications
  Getting it deployed Model interpretation Data storytelling
10:00-10:45am Lessons from:
Honeywell
Session on Deployment
Strategy
All level tracks
William Groves, Honeywell
Case Study: SmarterHQ
When Model Interpretation Matters: Understanding Complex Predictive Models

Dean Abbott, SmarterHQ
Insights from a Master Data Storyteller - "Everybody Lies" Author
Seth Stephens-Davidowitz, Author, NYTimes Opinion Writer
10:45-11:15am Exhibits & Morning Coffee Break
11:15-11:35am Workforce analytics Best practices Varied business applications
Lessons from:
Intel
How Intel Wins the Right Marketplace Talent with Analytics
All level tracks
Hai Harari, Intel
Q&A: Ask Dean and Karl Anything (about Best Practices)
Dean Abbott, SmarterHQ
Karl Rexer, Rexer Analytics
Case Study: BBC Worldwide
Catchy content: What makes TV content work?
David Boyle, BBC Worldwide
11:40am-12:00pm Industry-leading case studies
Customer Journey Analytics: Blazing Paths to Customer Success
Steven Ramirez, Beyone the Arc
12:00-1:10pm Lunch in Exhibit Hall
1:10-1:55pm
KEYNOTE
UPS' Road to Optimization

Jack Levis, UPS
1:55-2:15pm Diamond Sponsor Presentation
2:15-3:00pm Expert Panel
Women in Predictive Analytics: Opportunities and Challenge
Moderator: Anne Robinson, Verizon Wireless
Panelists: TBA
3:00-3:30pm Exhibits & Afternoon Break
  Track 1—BUSINESS: Analytics strategy & operationalization Track 2—TECH: Predictive modeling methods Track 3—MORE CASE STUDIES: Varied business applications
Building Data Science Teams Data quality PA adoption in a new industry
3:30-3:50pm Lessons from:
Comcast

Accelerating Data Science InnovationAll level tracks
Bob Bress, Comcast
Three Steps for Improving Data Quality for Predictive Analytics
Tom Redman, Data Quality Solutions
Case Study: RightShip
Overcoming Challenges Implementing a Risk Model in the Maritime Industry

Bryan Guenther, RightShip
3:55-4:15pm Agriculture analytics
Case Study: Circle A Farms
Advancing Hydroponics through IoT Analytics
All level tracks
Steve Fowler, Jivoo
4:15-5:00pm Model deployment Data policy Logistics analytics
Lessons from:
John Hancock

A Shiny Way to Operationalizing Analytics All level tracks
Shatrunjai Singh, John Hancock
Regulating Opacity: Solving for the Conflict Between Laws and Analytics
Andrew Burt, Immuta
Case Study: Cargonexx
Leveraging Machine Learning Techniques for Realtime Pricing in B2B Truck Logistics

Alwin Haensel, Haensel

Post-Conference Workshops: Wednesday, November 1, 2017
Full-day Workshop
Advanced Methods:
Data Preparation and Modeling Techniques

Dean Abbott, Abbott Analytics, Inc
Full-day Workshop
The Best and the Worst of Predictive Analytics:
Predictive Modeling Methods and Common Data Mining Mistakes

Dr. John Elder, Elder Research, Inc.
Full-day Workshop
Hadoop for Predictive Analytics: Hands-On Lab
James Casaletto, MapR Technologies


Post-Conference Workshop: Wednesday, November 2, 2017
Full-day Workshop
Supercharging Prediction with Ensemble Models
Dean Abbott, Abbott Analytics

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